Productivity is the symptom. OEE is the diagnosis.
The four patterns when they disagree — including the one where OEE falls and productivity rises, which most target systems actively punish.
Productivity and OEE are related in the way that a symptom and a diagnosis are related. Productivity tells you what came out per unit of input; OEE tells you where the equipment lost the difference. Neither substitutes for the other, and a plant that tracks only one is usually surprised by the other.
The distinction matters most when they disagree — which happens more often than people expect.
| Productivity | OEE | |
|---|---|---|
| Measures | Output per unit of input — per labour hour, per machine hour, per rupee | Equipment effectiveness against intent |
| Includes | Everything: people, method, scheduling, material, equipment | Equipment only, within planned production time |
| Improves when | Any input is used better, including by making different products | Equipment stops less, runs faster or scraps less |
| Owned by | Plant management | Production, maintenance and quality jointly |
| Diagnostic value | Low — tells you that something changed | High — tells you which of three things changed |
OEE up, productivity flat. The usual cause is improvement on a non-constraint machine. The equipment got better, the plant's output did not, because that machine was never the limit. This is the single most common way an improvement programme produces no commercial result, and it is entirely avoidable by identifying the constraint first.
Productivity up, OEE flat or down. Frequently a product-mix change — easier parts, longer runs, fewer changeovers. Output per hour rose for reasons that have nothing to do with equipment effectiveness. Worth recognising before anyone claims credit for an improvement initiative.
Both down. Usually one cause presenting twice, and the OEE split will say which of the three factors it is.
OEE down, productivity up. The interesting case. Often a deliberate trade — running slower to eliminate scrap, or stopping to fix a problem rather than producing through it. This is the pattern that a naive utilisation or OEE target actively punishes, which is a good argument for not setting targets on a single metric.
The workable arrangement is productivity as the plant-level outcome measure and OEE as the diagnostic underneath it. When productivity moves, OEE and its split tell you whether equipment was involved. When productivity does not move despite an OEE improvement, you have learned that you improved the wrong machine — which is worth knowing early.
Neither metric captures whether you made the right parts in the right order. That is schedule adherence, and it is a third question again, covered on the production monitoring page.